Full text: Papers accepted on the basis of peer-reviewed abstracts (Part B)

In: Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium - 100 Years ISPRS, Vienna, Austria, July 5-7, 2010, IAPRS, Vol. XXXVIII, Part 7B 
224 
Bastiaanssen, W. G. M. 2000. SEBAL - Based Sensible and 
Latent Heat Fluxes in the Irrigated Gediz Basin. Turkey. 
Journal of Hydrology, v. 229, p.87-100. 
Bins, L. S.; Fonseca, L. G.; Erthal, G. , 1996. Satellite 
imagery segmentation: a region growing approach. In: VIII 
Simposio Brasileiro de Sensoriamento Remoto, 1996. Anais. 
Sâo José dos Campos: INPE. 
Burba, G. G.; and Verma, S. B. Seasonal and interannual 
variability in évapotranspiration of native tallgrass prairie and 
cultivated wheat ecosystems. 2005. Agricultural and Forest 
Meteorology 135, p. 190-201. 
Cihlar, J. S.; St-Laurent,and L.; Dyer, J. A., 1991. Relation 
Between the Normalized Difference Vegetation Index and 
Ecological Variables, Remote Sensing and the Environment, 
v. 35, p.279-298. 
CONAB: Companhia Nacional de Abastecimento. 
Superintendência Regional do Paraná. Area e produçâo das 
safras paranaense e brasileira 2004/05 e 2005/06. 2005. 
Acesso em 21/08/2009. Disponivel em: 
http://www.conab.gov.br/conabweb/download/sureg/pr/soia/s 
oia novembro 2005.pdf 
Engman, E. T. 1995. Recent advances in remote sensing in 
hydrology. Reviews of Geophysics, v. 33, p. 967-975. 
GEOSAFRAS: PREVS AFRAS, 2007. Boletim de 
acompanhamento da safra de soja. Safra 2006/07- Paraná. 
CONAB. IAPAR. SIMEPAR. Ediçâo: 15 de fevereiro de 
2007. (Parcial), 20 p. 
Guillevic, P.; Koster, R. D.; Suarez, M. J.; Bounoua, L.; 
Collatz, G. J.; Los, S. O.; and Mahanama, S. P. P., 2002. 
Influence of the interannual variability of vegetation on the 
surface energy balance - A global sensitivity study. J. 
Hydrometeor., 3, 617-629. 
Heinemann, A.B.; Hoogenboom, G.; Faria, R.T.; 2002. 
Determination of spatial water requirements at county and 
regional levels using crop models and GIS. An example for 
the State of Parana, Brazil. Agricultural Water Management, 
52, 177-196. 
IAPAR, Cartas Climáticas do Paraná. Instituto Agronómico 
do Paraná. Londrina-PR, 2000. 
Jensen, M. E.; and Haise, H. R., 1963. Estimating 
évapotranspiration from solar radiation. Proc. American 
Society of Civil Engineering, J. Irrigation and Drainage 
Division. 89:15-41. 
Meinke, H.; and Stone, R. C. 2005. Seasonal and inter-annual 
climate forecasting: the new tool for increasing preparedness 
to climate variability and change in agricultural planning and 
operations. Climatic Change, v.70, p.221-253. 
Nobre, P.; Marengo, J.A.; Cavalcanti, I.F.A.; Obregon, G.; 
Barros, V.; Camilloni, I.; Campos, N.; and Ferreira, A.G., 
2006. Seasonal-to-Decadal Predictability and Prediction of 
South American Climate. J. Climate, 19, 5988-6004. 
Ogallo, L.; Bessemoulin, P.; Cerón, J.-P.; Mason, S.; and 
Connor, S. J., 2008: Adapting to climate variability and 
change: the Climate Outlook Forum process. BAMS, 57, 93- 
102. 
Rizzi, R.; Rudorff, B. F. T., 2005. Estimativa da area de soja 
no Rio Grande do Sul por meio de imagens Landsat. Revista 
Brasileira de Cartografia, a 57, v. 3, p. 226-234. 
Sarma, A.A.L.N.; and Lakshmi K. T.V., 2006. Studies on 
crop growing period and NDVI in relation to water balance 
components. Indian Journal of Radio & Space Physics, n. 35, 
p 424-434. 
Seiler, R.A.; Kogan, F.;. Guo, W.; and Vinocur, M., 2007. 
Seasonal and interannual responses of the vegetation and 
production of crops in Cordoba - Argentina assessed by 
AVHRR derived vegetation indices. Advances in Space 
Research, 39 (1) 00. 88-94. 
Semenov, M..A.; and Porter, J.R., 1995. Climatic variability 
and the modelling of crop yields. Agricultural and Forest 
Meteorology, v.73, p. 265-283. 
Shimabukuro, Y.E.; and Smith, J.A., 1991. The least-squares 
mixing models to generate fractio images derived from 
remote sensing multispectral data, IEEE Transactions on 
Geoscience and Remote Sensing, v. 29, n 1, p. 16-20. 
Suzuki, R.; Masuda, K.; and Dennis, D.G., 2007. Interannual 
covariability between actual évapotranspiration and PAL and 
GIMMS NDVIs of northern Asia. Remote Sensing of 
Environment, v.106, p. 387-398. 
Thornthwaite, C.W., 1948. An approach towards a rational 
classification of climate. Geographical Review, London, 
v.38, p.55-94. 
Thornthwaite, C. W.; and Mather, J. R., 1955. The water 
balance. Centerton: Drexel Institute of Technology. 104 p. 
(Publications in Climatology, v. 8, n. 1). 
Tucker, C. J.; Slayback, D. A.; Pinzon, J. E.; Los, S. O.; 
Myneni, R. B.; and Taylor, M. G., 2001. Higher northern 
latitude normalized difference vegetation index and growing 
season trends from 1982 to 1999. International Joumalof 
Biometeorology, 45, 184-190. 
Wagner, A.P L.; Weber, E.; Fontana, D.C.; Ducati, J.R.; and 
Klering, E., 2007. Estimativa de Area de Soja no Rio Grande 
do Sul Utilizando Imagens NDVEMODIS. Anais, XIII 
Simpôsio Brasileiro de Sensoriamento Remoto, INPE, 
Florianôpolis, p.457-464. 
Wrege, M. S.; Gonçalves, S. L.; Caramori, P. H.; 
Vasconeellos, M. E. C.; Oliveira, D.; Abuscarub Neto, M.; 
Borrozzino, E.; and Caviglione, J. H., 1999. Risco de 
deficiência hidrica na cultura do milho no estado do Parana. 
Pesquisa Agropecuaria Brasileira, Brasilia, v. 34, n.7.
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.